Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations15234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory511.4 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

host_id is highly overall correlated with idHigh correlation
id is highly overall correlated with host_idHigh correlation
latitude is highly overall correlated with neighbourhood_groupHigh correlation
longitude is highly overall correlated with neighbourhood_groupHigh correlation
neighbourhood_group is highly overall correlated with latitude and 1 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_monthHigh correlation
price is highly overall correlated with room_typeHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
room_type is highly overall correlated with priceHigh correlation
minimum_nights is highly skewed (γ1 = 33.03410305) Skewed
id has unique values Unique
availability_365 has 5073 (33.3%) zeros Zeros

Reproduction

Analysis started2025-08-26 18:25:21.110295
Analysis finished2025-08-26 18:25:26.786443
Duration5.68 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct15234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17945618
Minimum2539
Maximum36442252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:26.830346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2539
5-th percentile875407.4
Q18491858.5
median18662582
Q327394790
95-th percentile34302650
Maximum36442252
Range36439713
Interquartile range (IQR)18902931

Descriptive statistics

Standard deviation10701397
Coefficient of variation (CV)0.5963237
Kurtosis-1.2042241
Mean17945618
Median Absolute Deviation (MAD)9384715
Skewness-0.0524836
Sum2.7338354 × 1011
Variance1.145199 × 1014
MonotonicityNot monotonic
2025-08-26T21:25:26.897282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9138664 1
 
< 0.1%
24588491 1
 
< 0.1%
4697809 1
 
< 0.1%
11652590 1
 
< 0.1%
1578103 1
 
< 0.1%
8382660 1
 
< 0.1%
34274062 1
 
< 0.1%
15314886 1
 
< 0.1%
22886125 1
 
< 0.1%
8899476 1
 
< 0.1%
Other values (15224) 15224
99.9%
ValueCountFrequency (%)
2539 1
< 0.1%
3831 1
< 0.1%
5022 1
< 0.1%
5121 1
< 0.1%
5203 1
< 0.1%
5238 1
< 0.1%
5803 1
< 0.1%
6090 1
< 0.1%
6848 1
< 0.1%
7801 1
< 0.1%
ValueCountFrequency (%)
36442252 1
< 0.1%
36425863 1
< 0.1%
36411407 1
< 0.1%
36351543 1
< 0.1%
36351128 1
< 0.1%
36318560 1
< 0.1%
36309284 1
< 0.1%
36308600 1
< 0.1%
36280646 1
< 0.1%
36273046 1
< 0.1%

name
Text

Distinct15101
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-08-26T21:25:27.113553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length98
Median length54
Mean length36.822108
Min length1

Characters and Unicode

Total characters560948
Distinct characters453
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14999 ?
Unique (%)98.5%

Sample

1st rowPrivate Lg Room 15 min to Manhattan
2nd rowVoted #1 Location Quintessential 1BR W Village Apt
3rd rowSpacious 1 bedroom apartment 15min from Manhattan
4th rowBig beautiful bedroom in huge Bushwick apartment
5th rowLRG 2br BKLYN APT CLOSE TO TRAINS AND PARK
ValueCountFrequency (%)
in 5266
 
5.7%
room 3226
 
3.5%
2516
 
2.7%
private 2381
 
2.6%
bedroom 2377
 
2.6%
apartment 2109
 
2.3%
cozy 1709
 
1.8%
apt 1446
 
1.6%
brooklyn 1349
 
1.5%
to 1296
 
1.4%
Other values (5747) 69080
74.5%
2025-08-26T21:25:27.416417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78056
 
13.9%
e 38568
 
6.9%
o 38503
 
6.9%
t 33045
 
5.9%
a 32568
 
5.8%
r 30748
 
5.5%
i 29726
 
5.3%
n 29434
 
5.2%
l 16034
 
2.9%
m 15566
 
2.8%
Other values (443) 218700
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 560948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
78056
 
13.9%
e 38568
 
6.9%
o 38503
 
6.9%
t 33045
 
5.9%
a 32568
 
5.8%
r 30748
 
5.5%
i 29726
 
5.3%
n 29434
 
5.2%
l 16034
 
2.9%
m 15566
 
2.8%
Other values (443) 218700
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 560948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
78056
 
13.9%
e 38568
 
6.9%
o 38503
 
6.9%
t 33045
 
5.9%
a 32568
 
5.8%
r 30748
 
5.5%
i 29726
 
5.3%
n 29434
 
5.2%
l 16034
 
2.9%
m 15566
 
2.8%
Other values (443) 218700
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 560948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
78056
 
13.9%
e 38568
 
6.9%
o 38503
 
6.9%
t 33045
 
5.9%
a 32568
 
5.8%
r 30748
 
5.5%
i 29726
 
5.3%
n 29434
 
5.2%
l 16034
 
2.9%
m 15566
 
2.8%
Other values (443) 218700
39.0%

host_id
Real number (ℝ)

High correlation 

Distinct13242
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63388582
Minimum2571
Maximum2.7384167 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:27.493400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2571
5-th percentile663838.75
Q16998297.2
median28271574
Q398704156
95-th percentile2.2914738 × 108
Maximum2.7384167 × 108
Range2.738391 × 108
Interquartile range (IQR)91705858

Descriptive statistics

Standard deviation75183373
Coefficient of variation (CV)1.1860712
Kurtosis0.40033208
Mean63388582
Median Absolute Deviation (MAD)25362042
Skewness1.2756949
Sum9.6566166 × 1011
Variance5.6525395 × 1015
MonotonicityNot monotonic
2025-08-26T21:25:27.559372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219517861 72
 
0.5%
61391963 29
 
0.2%
16098958 21
 
0.1%
30283594 19
 
0.1%
1475015 18
 
0.1%
7503643 18
 
0.1%
137358866 18
 
0.1%
119669058 17
 
0.1%
190921808 16
 
0.1%
22541573 16
 
0.1%
Other values (13232) 14990
98.4%
ValueCountFrequency (%)
2571 1
 
< 0.1%
2787 3
< 0.1%
3151 1
 
< 0.1%
3415 1
 
< 0.1%
3563 1
 
< 0.1%
3647 2
< 0.1%
4396 1
 
< 0.1%
4869 1
 
< 0.1%
5089 1
 
< 0.1%
6041 1
 
< 0.1%
ValueCountFrequency (%)
273841667 1
< 0.1%
273361532 1
< 0.1%
272872092 1
< 0.1%
272557707 1
< 0.1%
272327753 1
< 0.1%
272314085 1
< 0.1%
271901058 1
< 0.1%
271885652 1
< 0.1%
271867677 1
< 0.1%
271616449 1
< 0.1%
Distinct5480
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Memory size942.3 KiB
2025-08-26T21:25:27.712153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length6.11389
Min length1

Characters and Unicode

Total characters93139
Distinct characters114
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3591 ?
Unique (%)23.6%

Sample

1st rowIris
2nd rowJohn
3rd rowRegan
4th rowMegan
5th rowJenny
ValueCountFrequency (%)
360
 
2.1%
and 224
 
1.3%
michael 138
 
0.8%
david 131
 
0.8%
john 124
 
0.7%
alex 103
 
0.6%
laura 87
 
0.5%
maria 82
 
0.5%
sonder 81
 
0.5%
nyc 75
 
0.4%
Other values (5096) 15650
91.8%
2025-08-26T21:25:27.932383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11911
 
12.8%
e 8962
 
9.6%
i 7664
 
8.2%
n 7590
 
8.1%
r 5549
 
6.0%
l 4761
 
5.1%
o 3934
 
4.2%
t 2928
 
3.1%
s 2923
 
3.1%
h 2814
 
3.0%
Other values (104) 34103
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11911
 
12.8%
e 8962
 
9.6%
i 7664
 
8.2%
n 7590
 
8.1%
r 5549
 
6.0%
l 4761
 
5.1%
o 3934
 
4.2%
t 2928
 
3.1%
s 2923
 
3.1%
h 2814
 
3.0%
Other values (104) 34103
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11911
 
12.8%
e 8962
 
9.6%
i 7664
 
8.2%
n 7590
 
8.1%
r 5549
 
6.0%
l 4761
 
5.1%
o 3934
 
4.2%
t 2928
 
3.1%
s 2923
 
3.1%
h 2814
 
3.0%
Other values (104) 34103
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11911
 
12.8%
e 8962
 
9.6%
i 7664
 
8.2%
n 7590
 
8.1%
r 5549
 
6.0%
l 4761
 
5.1%
o 3934
 
4.2%
t 2928
 
3.1%
s 2923
 
3.1%
h 2814
 
3.0%
Other values (104) 34103
36.6%

neighbourhood_group
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size969.2 KiB
Brooklyn
6578 
Manhattan
6279 
Queens
1893 
Bronx
 
346
Staten Island
 
138

Length

Max length13
Median length9
Mean length8.1408035
Min length5

Characters and Unicode

Total characters124017
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQueens
2nd rowManhattan
3rd rowQueens
4th rowBrooklyn
5th rowBrooklyn

Common Values

ValueCountFrequency (%)
Brooklyn 6578
43.2%
Manhattan 6279
41.2%
Queens 1893
 
12.4%
Bronx 346
 
2.3%
Staten Island 138
 
0.9%

Length

2025-08-26T21:25:28.004680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-26T21:25:28.063023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 6578
42.8%
manhattan 6279
40.8%
queens 1893
 
12.3%
bronx 346
 
2.3%
staten 138
 
0.9%
island 138
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n 21651
17.5%
a 19113
15.4%
o 13502
10.9%
t 12834
10.3%
r 6924
 
5.6%
B 6924
 
5.6%
l 6716
 
5.4%
y 6578
 
5.3%
k 6578
 
5.3%
M 6279
 
5.1%
Other values (10) 16918
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 124017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 21651
17.5%
a 19113
15.4%
o 13502
10.9%
t 12834
10.3%
r 6924
 
5.6%
B 6924
 
5.6%
l 6716
 
5.4%
y 6578
 
5.3%
k 6578
 
5.3%
M 6279
 
5.1%
Other values (10) 16918
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 124017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 21651
17.5%
a 19113
15.4%
o 13502
10.9%
t 12834
10.3%
r 6924
 
5.6%
B 6924
 
5.6%
l 6716
 
5.4%
y 6578
 
5.3%
k 6578
 
5.3%
M 6279
 
5.1%
Other values (10) 16918
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 124017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 21651
17.5%
a 19113
15.4%
o 13502
10.9%
t 12834
10.3%
r 6924
 
5.6%
B 6924
 
5.6%
l 6716
 
5.4%
y 6578
 
5.3%
k 6578
 
5.3%
M 6279
 
5.1%
Other values (10) 16918
13.6%
Distinct211
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2025-08-26T21:25:28.237251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length26
Median length17
Mean length11.940659
Min length4

Characters and Unicode

Total characters181904
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st rowSunnyside
2nd rowWest Village
3rd rowAstoria
4th rowBushwick
5th rowProspect-Lefferts Gardens
ValueCountFrequency (%)
east 2121
 
8.7%
side 1378
 
5.6%
harlem 1300
 
5.3%
williamsburg 1246
 
5.1%
bedford-stuyvesant 1245
 
5.1%
heights 1156
 
4.7%
upper 1084
 
4.4%
village 954
 
3.9%
west 806
 
3.3%
bushwick 774
 
3.2%
Other values (224) 12455
50.8%
2025-08-26T21:25:28.476155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 16604
 
9.1%
i 12747
 
7.0%
s 12671
 
7.0%
t 12049
 
6.6%
a 12028
 
6.6%
l 10770
 
5.9%
r 10669
 
5.9%
9285
 
5.1%
n 8172
 
4.5%
o 7529
 
4.1%
Other values (44) 69380
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16604
 
9.1%
i 12747
 
7.0%
s 12671
 
7.0%
t 12049
 
6.6%
a 12028
 
6.6%
l 10770
 
5.9%
r 10669
 
5.9%
9285
 
5.1%
n 8172
 
4.5%
o 7529
 
4.1%
Other values (44) 69380
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16604
 
9.1%
i 12747
 
7.0%
s 12671
 
7.0%
t 12049
 
6.6%
a 12028
 
6.6%
l 10770
 
5.9%
r 10669
 
5.9%
9285
 
5.1%
n 8172
 
4.5%
o 7529
 
4.1%
Other values (44) 69380
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16604
 
9.1%
i 12747
 
7.0%
s 12671
 
7.0%
t 12049
 
6.6%
a 12028
 
6.6%
l 10770
 
5.9%
r 10669
 
5.9%
9285
 
5.1%
n 8172
 
4.5%
o 7529
 
4.1%
Other values (44) 69380
38.1%

latitude
Real number (ℝ)

High correlation 

Distinct10443
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.727492
Minimum40.50873
Maximum40.91306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:28.548892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40.50873
5-th percentile40.643912
Q140.68773
median40.72075
Q340.763257
95-th percentile40.826985
Maximum40.91306
Range0.40433
Interquartile range (IQR)0.0755275

Descriptive statistics

Standard deviation0.05592842
Coefficient of variation (CV)0.0013732351
Kurtosis0.029537755
Mean40.727492
Median Absolute Deviation (MAD)0.036775
Skewness0.26961298
Sum620442.62
Variance0.0031279882
MonotonicityNot monotonic
2025-08-26T21:25:28.617028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71813 7
 
< 0.1%
40.68084 7
 
< 0.1%
40.69414 7
 
< 0.1%
40.72232 7
 
< 0.1%
40.70732 6
 
< 0.1%
40.75607 6
 
< 0.1%
40.69054 6
 
< 0.1%
40.71532 6
 
< 0.1%
40.68237 6
 
< 0.1%
40.70563 6
 
< 0.1%
Other values (10433) 15170
99.6%
ValueCountFrequency (%)
40.50873 1
< 0.1%
40.52293 1
< 0.1%
40.53871 1
< 0.1%
40.54106 1
< 0.1%
40.54312 1
< 0.1%
40.5455 1
< 0.1%
40.54857 1
< 0.1%
40.54889 1
< 0.1%
40.54901 1
< 0.1%
40.55182 1
< 0.1%
ValueCountFrequency (%)
40.91306 1
< 0.1%
40.90391 1
< 0.1%
40.90356 1
< 0.1%
40.90329 1
< 0.1%
40.89981 1
< 0.1%
40.89811 1
< 0.1%
40.89756 1
< 0.1%
40.89747 1
< 0.1%
40.896 1
< 0.1%
40.89581 1
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct8854
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.949915
Minimum-74.23914
Maximum-73.72582
Zeros0
Zeros (%)0.0%
Negative15234
Negative (%)100.0%
Memory size119.1 KiB
2025-08-26T21:25:28.681423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-74.23914
5-th percentile-74.003314
Q1-73.98152
median-73.95395
Q3-73.93335
95-th percentile-73.857793
Maximum-73.72582
Range0.51332
Interquartile range (IQR)0.04817

Descriptive statistics

Standard deviation0.047625463
Coefficient of variation (CV)-0.00064402322
Kurtosis4.6515667
Mean-73.949915
Median Absolute Deviation (MAD)0.024945
Skewness1.1877173
Sum-1126553
Variance0.0022681847
MonotonicityNot monotonic
2025-08-26T21:25:28.741961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.95742 9
 
0.1%
-73.94829 8
 
0.1%
-73.95427 8
 
0.1%
-73.95121 8
 
0.1%
-73.9398 8
 
0.1%
-73.95688 7
 
< 0.1%
-73.95149 7
 
< 0.1%
-73.94421 7
 
< 0.1%
-73.95332 7
 
< 0.1%
-73.95509 7
 
< 0.1%
Other values (8844) 15158
99.5%
ValueCountFrequency (%)
-74.23914 1
< 0.1%
-74.21238 1
< 0.1%
-74.19626 1
< 0.1%
-74.18259 1
< 0.1%
-74.17628 1
< 0.1%
-74.17388 1
< 0.1%
-74.17117 1
< 0.1%
-74.17065 1
< 0.1%
-74.16966 1
< 0.1%
-74.16634 1
< 0.1%
ValueCountFrequency (%)
-73.72582 1
< 0.1%
-73.72716 1
< 0.1%
-73.72778 1
< 0.1%
-73.72817 1
< 0.1%
-73.72901 1
< 0.1%
-73.72928 1
< 0.1%
-73.72931 1
< 0.1%
-73.72943 1
< 0.1%
-73.72956 1
< 0.1%
-73.72982 1
< 0.1%

room_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Entire home/apt
7756 
Private room
7159 
Shared room
 
319

Length

Max length15
Median length15
Mean length13.506433
Min length11

Characters and Unicode

Total characters205757
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowPrivate room
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 7756
50.9%
Private room 7159
47.0%
Shared room 319
 
2.1%

Length

2025-08-26T21:25:28.801282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-26T21:25:28.849509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
entire 7756
25.5%
home/apt 7756
25.5%
room 7478
24.5%
private 7159
23.5%
shared 319
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 22990
11.2%
o 22712
11.0%
r 22712
11.0%
t 22671
11.0%
a 15234
 
7.4%
15234
 
7.4%
m 15234
 
7.4%
i 14915
 
7.2%
h 8075
 
3.9%
p 7756
 
3.8%
Other values (7) 38224
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 205757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 22990
11.2%
o 22712
11.0%
r 22712
11.0%
t 22671
11.0%
a 15234
 
7.4%
15234
 
7.4%
m 15234
 
7.4%
i 14915
 
7.2%
h 8075
 
3.9%
p 7756
 
3.8%
Other values (7) 38224
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 205757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 22990
11.2%
o 22712
11.0%
r 22712
11.0%
t 22671
11.0%
a 15234
 
7.4%
15234
 
7.4%
m 15234
 
7.4%
i 14915
 
7.2%
h 8075
 
3.9%
p 7756
 
3.8%
Other values (7) 38224
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 205757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 22990
11.2%
o 22712
11.0%
r 22712
11.0%
t 22671
11.0%
a 15234
 
7.4%
15234
 
7.4%
m 15234
 
7.4%
i 14915
 
7.2%
h 8075
 
3.9%
p 7756
 
3.8%
Other values (7) 38224
18.6%

price
Real number (ℝ)

High correlation 

Distinct301
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.15268
Minimum10
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.012582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile40
Q166
median100
Q3155
95-th percentile255
Maximum350
Range340
Interquartile range (IQR)89

Descriptive statistics

Standard deviation69.252428
Coefficient of variation (CV)0.57637021
Kurtosis0.67756296
Mean120.15268
Median Absolute Deviation (MAD)41
Skewness1.064435
Sum1830406
Variance4795.8988
MonotonicityNot monotonic
2025-08-26T21:25:29.077094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 644
 
4.2%
100 634
 
4.2%
50 498
 
3.3%
75 454
 
3.0%
60 441
 
2.9%
200 426
 
2.8%
80 419
 
2.8%
70 393
 
2.6%
65 392
 
2.6%
120 390
 
2.6%
Other values (291) 10543
69.2%
ValueCountFrequency (%)
10 3
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
16 3
 
< 0.1%
19 3
 
< 0.1%
20 10
0.1%
21 3
 
< 0.1%
22 6
< 0.1%
23 1
 
< 0.1%
24 7
< 0.1%
ValueCountFrequency (%)
350 98
0.6%
349 13
 
0.1%
346 1
 
< 0.1%
345 9
 
0.1%
344 1
 
< 0.1%
342 1
 
< 0.1%
341 1
 
< 0.1%
340 4
 
< 0.1%
339 4
 
< 0.1%
336 1
 
< 0.1%

minimum_nights
Real number (ℝ)

Skewed 

Distinct66
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8846002
Minimum1
Maximum1250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.141846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile30
Maximum1250
Range1249
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.096464
Coefficient of variation (CV)3.245159
Kurtosis1752.4487
Mean5.8846002
Median Absolute Deviation (MAD)1
Skewness33.034103
Sum89646
Variance364.67492
MonotonicityNot monotonic
2025-08-26T21:25:29.210021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4106
27.0%
1 3892
25.5%
3 2647
17.4%
4 1043
 
6.8%
5 923
 
6.1%
30 909
 
6.0%
7 588
 
3.9%
6 240
 
1.6%
14 142
 
0.9%
10 135
 
0.9%
Other values (56) 609
 
4.0%
ValueCountFrequency (%)
1 3892
25.5%
2 4106
27.0%
3 2647
17.4%
4 1043
 
6.8%
5 923
 
6.1%
6 240
 
1.6%
7 588
 
3.9%
8 42
 
0.3%
9 25
 
0.2%
10 135
 
0.9%
ValueCountFrequency (%)
1250 1
 
< 0.1%
999 1
 
< 0.1%
370 1
 
< 0.1%
365 8
0.1%
364 1
 
< 0.1%
240 1
 
< 0.1%
200 1
 
< 0.1%
198 1
 
< 0.1%
180 9
0.1%
160 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

High correlation 

Distinct320
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.65531
Minimum1
Maximum607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.273163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q333
95-th percentile131
Maximum607
Range606
Interquartile range (IQR)30

Descriptive statistics

Standard deviation49.044845
Coefficient of variation (CV)1.6538301
Kurtosis16.13148
Mean29.65531
Median Absolute Deviation (MAD)8
Skewness3.3557735
Sum451769
Variance2405.3968
MonotonicityNot monotonic
2025-08-26T21:25:29.334848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2020
 
13.3%
2 1342
 
8.8%
3 974
 
6.4%
4 796
 
5.2%
5 588
 
3.9%
6 543
 
3.6%
7 495
 
3.2%
8 449
 
2.9%
9 383
 
2.5%
10 330
 
2.2%
Other values (310) 7314
48.0%
ValueCountFrequency (%)
1 2020
13.3%
2 1342
8.8%
3 974
6.4%
4 796
 
5.2%
5 588
 
3.9%
6 543
 
3.6%
7 495
 
3.2%
8 449
 
2.9%
9 383
 
2.5%
10 330
 
2.2%
ValueCountFrequency (%)
607 1
< 0.1%
594 1
< 0.1%
510 1
< 0.1%
488 1
< 0.1%
474 1
< 0.1%
467 1
< 0.1%
466 1
< 0.1%
459 1
< 0.1%
448 1
< 0.1%
441 1
< 0.1%
Distinct1494
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
Minimum2011-05-12 00:00:00
Maximum2019-07-08 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-26T21:25:29.397589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:29.463741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation 

Distinct789
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3809039
Minimum0.01
Maximum27.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.523701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.19
median0.72
Q32.01
95-th percentile4.69
Maximum27.95
Range27.94
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.6898504
Coefficient of variation (CV)1.2237277
Kurtosis11.961705
Mean1.3809039
Median Absolute Deviation (MAD)0.62
Skewness2.4361425
Sum21036.69
Variance2.8555944
MonotonicityNot monotonic
2025-08-26T21:25:29.584235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 360
 
2.4%
1 352
 
2.3%
0.05 333
 
2.2%
0.03 323
 
2.1%
0.04 268
 
1.8%
0.08 256
 
1.7%
0.16 244
 
1.6%
0.09 235
 
1.5%
0.06 226
 
1.5%
0.11 217
 
1.4%
Other values (779) 12420
81.5%
ValueCountFrequency (%)
0.01 17
 
0.1%
0.02 360
2.4%
0.03 323
2.1%
0.04 268
1.8%
0.05 333
2.2%
0.06 226
1.5%
0.07 167
1.1%
0.08 256
1.7%
0.09 235
1.5%
0.1 191
1.3%
ValueCountFrequency (%)
27.95 1
< 0.1%
20.94 1
< 0.1%
19.75 1
< 0.1%
17.82 1
< 0.1%
16.22 1
< 0.1%
13.45 1
< 0.1%
13.42 1
< 0.1%
13.24 1
< 0.1%
13.15 1
< 0.1%
12.99 1
< 0.1%
Distinct46
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.788762
Minimum1
Maximum327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.643250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile9
Maximum327
Range326
Interquartile range (IQR)1

Descriptive statistics

Standard deviation24.519675
Coefficient of variation (CV)5.1202535
Kurtosis141.75031
Mean4.788762
Median Absolute Deviation (MAD)0
Skewness11.41446
Sum72952
Variance601.21448
MonotonicityNot monotonic
2025-08-26T21:25:29.703156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 9987
65.6%
2 2259
 
14.8%
3 988
 
6.5%
4 482
 
3.2%
5 300
 
2.0%
6 182
 
1.2%
7 135
 
0.9%
8 129
 
0.8%
9 79
 
0.5%
327 72
 
0.5%
Other values (36) 621
 
4.1%
ValueCountFrequency (%)
1 9987
65.6%
2 2259
 
14.8%
3 988
 
6.5%
4 482
 
3.2%
5 300
 
2.0%
6 182
 
1.2%
7 135
 
0.9%
8 129
 
0.8%
9 79
 
0.5%
10 50
 
0.3%
ValueCountFrequency (%)
327 72
0.5%
232 4
 
< 0.1%
121 19
 
0.1%
103 18
 
0.1%
96 30
0.2%
91 29
0.2%
87 16
 
0.1%
52 36
0.2%
50 15
 
0.1%
49 3
 
< 0.1%

availability_365
Real number (ℝ)

Zeros 

Distinct366
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.72391
Minimum0
Maximum365
Zeros5073
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size119.1 KiB
2025-08-26T21:25:29.767025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51
Q3224
95-th percentile354
Maximum365
Range365
Interquartile range (IQR)224

Descriptive statistics

Standard deviation129.09634
Coefficient of variation (CV)1.1452436
Kurtosis-0.98837262
Mean112.72391
Median Absolute Deviation (MAD)51
Skewness0.75240675
Sum1717236
Variance16665.864
MonotonicityNot monotonic
2025-08-26T21:25:29.834886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5073
33.3%
365 296
 
1.9%
1 156
 
1.0%
364 127
 
0.8%
5 111
 
0.7%
3 104
 
0.7%
2 100
 
0.7%
4 97
 
0.6%
6 91
 
0.6%
90 85
 
0.6%
Other values (356) 8994
59.0%
ValueCountFrequency (%)
0 5073
33.3%
1 156
 
1.0%
2 100
 
0.7%
3 104
 
0.7%
4 97
 
0.6%
5 111
 
0.7%
6 91
 
0.6%
7 81
 
0.5%
8 75
 
0.5%
9 72
 
0.5%
ValueCountFrequency (%)
365 296
1.9%
364 127
0.8%
363 65
 
0.4%
362 47
 
0.3%
361 33
 
0.2%
360 28
 
0.2%
359 40
 
0.3%
358 39
 
0.3%
357 29
 
0.2%
356 24
 
0.2%

Interactions

2025-08-26T21:25:26.104422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.588743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.090453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.552498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.236704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.677737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.144079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.632057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.092772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.538110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.151300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.645869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.134430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.733368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.282828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.727098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.194280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.680065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.138115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.585538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.199181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.692028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.180288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.815357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.325836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.771213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.241365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.725164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.181779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.628165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.252573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.745528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.232619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.891276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.372840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.821696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.294049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.778588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.231373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.784747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.297171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.794086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.279524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.941766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.411848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.865033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.340842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.823657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.273789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.829943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.345975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.846844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.327209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.992768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.456490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.911213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.390326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.871768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.320543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.875679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.394004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.897250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.371138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.041163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.502735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.956433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.439612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.916951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.364534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.920192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.441341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.949356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.415783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.088670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.546908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.002169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.486801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.961247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.409335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.966339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.486316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:21.995980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.460361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.134441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.587474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.047862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.533416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.003525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.449729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.011422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.536030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.043946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:22.505675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.186004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:23.632134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.094873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:24.580956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.048068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:25.493952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2025-08-26T21:25:26.056385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2025-08-26T21:25:29.886116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsneighbourhood_groupnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.3710.1230.066-0.0350.1040.0160.0840.3040.0390.3970.104
calculated_host_listings_count0.3711.0000.1210.091-0.0170.109-0.0050.0640.088-0.1770.1530.071
host_id0.1230.1211.0000.5650.0220.142-0.1980.099-0.091-0.1230.2720.105
id0.0660.0910.5651.000-0.0210.100-0.1680.060-0.294-0.0780.3650.084
latitude-0.035-0.0170.022-0.0211.0000.0540.0120.544-0.0190.112-0.0300.100
longitude0.1040.1090.1420.1000.0541.000-0.1250.6530.065-0.4150.1280.129
minimum_nights0.016-0.005-0.198-0.1680.012-0.1251.0000.000-0.1640.117-0.2930.000
neighbourhood_group0.0840.0640.0990.0600.5440.6530.0001.0000.0260.1750.0670.099
number_of_reviews0.3040.088-0.091-0.294-0.0190.065-0.1640.0261.0000.0130.7070.000
price0.039-0.177-0.123-0.0780.112-0.4150.1170.1750.0131.000-0.0200.501
reviews_per_month0.3970.1530.2720.365-0.0300.128-0.2930.0670.707-0.0201.0000.015
room_type0.1040.0710.1050.0840.1000.1290.0000.0990.0000.5010.0151.000

Missing values

2025-08-26T21:25:26.607552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-26T21:25:26.718819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
09138664Private Lg Room 15 min to Manhattan47594947IrisQueensSunnyside40.74271-73.92493Private room74262019-05-260.1315
18741020Voted #1 Location Quintessential 1BR W Village Apt45854238JohnManhattanWest Village40.73631-74.00611Entire home/apt2453512018-09-191.1210
234602077Spacious 1 bedroom apartment 15min from Manhattan261055465ReganQueensAstoria40.76424-73.92351Entire home/apt125312019-05-240.65113
323203149Big beautiful bedroom in huge Bushwick apartment143460MeganBrooklynBushwick40.69839-73.92044Private room65282019-06-230.5228
44402805LRG 2br BKLYN APT CLOSE TO TRAINS AND PARK22807362JennyBrooklynProspect-Lefferts Gardens40.66025-73.96270Entire home/apt120332018-08-280.05116
530070126✩Prime Renovated 1/1 Apartment in Upper East Side✩4968673SeanManhattanUpper East Side40.76831-73.95929Entire home/apt200522019-05-260.68171
634231172Fully renovated brick house floor in Brooklyn59642348KevinBrooklynSunset Park40.64550-74.01262Entire home/apt95192019-07-089.001106
75856760Renovated 1BR in exciting, convenient area29408349ChadManhattanChinatown40.71490-73.99976Entire home/apt179572017-04-180.1410
87929441Beautiful Loft w/ Waterfront View!1453898AnthonyBrooklynWilliamsburg40.71268-73.96676Private room10522322019-06-195.00364
918340498Private bedroom and bathroom near Prospect Park33723491SuzzanneBrooklynFlatbush40.65274-73.95848Private room39272017-09-290.2710
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
1522426029571Bright, spacious one bedroom in Brooklyn7193111TanyaBrooklynCrown Heights40.67639-73.94714Entire home/apt2001152019-06-121.4212
152251327940Huge Gorgeous Park View Apartment!3290436HadarBrooklynFlatbush40.65335-73.96257Entire home/apt1203132016-08-270.282327
1522623612681Shared Room 1 Stop from Manhattan on the F Train55724558TaylorQueensLong Island City40.76006-73.94080Private room55422019-06-010.65589
1522734485745Midtown Manhattan Stunner - Private room261632622RoyaltonManhattanTheater District40.75491-73.98507Private room100132019-06-163.009318
1522825616250Stylish, spacious, private 1BR apt in Ditmas Park125396920AdamBrooklynFlatbush40.64314-73.95705Entire home/apt753102019-01-030.8410
152297094539Tranquil haven in bubbly Brooklyn2052211AdrianaBrooklynWindsor Terrace40.65360-73.97546Entire home/apt1431422016-08-270.04110
152304424261Large 1 BR with backyard on UWS3447311SarahManhattanUpper West Side40.80188-73.96808Entire home/apt2002222019-05-210.5010
152314545882Amazing studio/Loft with a backyard23569951KavehManhattanUpper East Side40.78110-73.94567Entire home/apt2203282019-05-230.501293
1523226518547U2 comfortable double bed sleeps 2 guests295128Carol GloriaBronxClason Point40.81225-73.85502Private room80142019-07-011.487365
1523333631782Private Bedroom in Williamsburg Apt!8569221AndiBrooklynWilliamsburg40.71829-73.95819Private room109332019-04-281.07297